A Benchmark for Breast Ultrasound Image Classification

Shareef, Bryar; Xian, Min; Sun, Shoukun; Vakanski, Aleksandar; Ding, Jianrui; Ning, Chunping; Cheng, Heng-Da

doi:10.2139/ssrn.4339660

Cited by 5 publications

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“…Breast ultrasound (BUS) image processing algorithms have been proposed in the last two decades, but the performances of most approaches have been assessed using relatively small private datasets with different quantitative metrics. Benchmark for Breast Ultrasound Image Segmentation (BUSIS) [ 38 ] provides a benchmark to compare existing methods objectively, and to determine the performance of the best breast tumor segmentation and classification algorithms. BUSIS is a comprehensive BUS image dataset that includes five individual datasets: the HMSS dataset, Thammasat dataset, BUSIS dataset, Dataset B, and BUSI dataset.…”

Section: Resultsmentioning

confidence: 99%

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A Novel Fuzzy Relative-Position-Coding Transformer for Breast Cancer Diagnosis Using Ultrasonography

Guo,

Jiang,

et al. 2023

Healthcare

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Breast cancer is a leading cause of death in women worldwide, and early detection is crucial for successful treatment. Computer-aided diagnosis (CAD) systems have been developed to assist doctors in identifying breast cancer on ultrasound images. In this paper, we propose a novel fuzzy relative-position-coding (FRPC) Transformer to classify breast ultrasound (BUS) images for breast cancer diagnosis. The proposed FRPC Transformer utilizes the self-attention mechanism of Transformer networks combined with fuzzy relative-position-coding to capture global and local features of the BUS images. The performance of the proposed method is evaluated on one benchmark dataset and compared with those obtained by existing Transformer approaches using various metrics. The experimental outcomes distinctly establish the superiority of the proposed method in achieving elevated levels of accuracy, sensitivity, specificity, and F1 score (all at 90.52%), as well as a heightened area under the receiver operating characteristic (ROC) curve (0.91), surpassing those attained by the original Transformer model (at 89.54%, 89.54%, 89.54%, and 0.89, respectively). Overall, the proposed FRPC Transformer is a promising approach for breast cancer diagnosis. It has potential applications in clinical practice and can contribute to the early detection of breast cancer.

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Section: Resultsmentioning

confidence: 99%

“…We conducted experiments on a large public breast ultrasound image dataset, the BUSIS dataset [ 38 ]. The experimental results demonstrate that our proposed FRPC Transformer achieved a better performance than the original Swin Transformer method.…”

Section: Discussionmentioning

confidence: 99%

A Novel Fuzzy Relative-Position-Coding Transformer for Breast Cancer Diagnosis Using Ultrasonography

Guo,

Jiang,

et al. 2023

Healthcare

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“…We identified six publicly available BUS datasets, whose main characteristics are summarized in Table 5. Indeed, some of these datasets have been merged in some works to increase the number of cases for evaluating lesion segmentation and classification methods 33,58 …”

Section: Discussionmentioning

confidence: 99%

“…Indeed, some of these datasets have been merged in some works to increase the number of cases for evaluating lesion segmentation and classification methods. 33,58 The largest dataset is US Cases, with 2006 cases; the smallest is OASBUD, with 100 cases. All the datasets except BUSIS have pathology classes proven by biopsy.…”

Section: Comparison With Existing Bus Datasetsmentioning

confidence: 99%

BUS‐BRA: A breast ultrasound dataset for assessing computer‐aided diagnosis systems

Gómez‐Flores,

Gregorio‐Calas,

Coelho de Albuquerque Pereira

2023

Medical Physics

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PurposeComputer‐aided diagnosis (CAD) systems on breast ultrasound (BUS) aim to increase the efficiency and effectiveness of breast screening, helping specialists to detect and classify breast lesions. CAD system development requires a set of annotated images, including lesion segmentation, biopsy results to specify benign and malignant cases, and BI‐RADS categories to indicate the likelihood of malignancy. Besides, standardized partitions of training, validation, and test sets promote reproducibility and fair comparisons between different approaches. Thus, we present a publicly available BUS dataset whose novelty is the substantial increment of cases with the above‐mentioned annotations and the inclusion of standardized partitions to objectively assess and compare CAD systems.Acquisition and Validation MethodsThe BUS dataset comprises 1875 anonymized images from 1064 female patients acquired via four ultrasound scanners during systematic studies at the National Institute of Cancer (Rio de Janeiro, Brazil). The dataset includes biopsy‐proven tumors divided into 722 benign and 342 malignant cases. Besides, a senior ultrasonographer performed a BI‐RADS assessment in categories 2 to 5. Additionally, the ultrasonographer manually outlined the breast lesions to obtain ground truth segmentations. Furthermore, 5‐ and 10‐fold cross‐validation partitions are provided to standardize the training and test sets to evaluate and reproduce CAD systems. Finally, to validate the utility of the BUS dataset, an evaluation framework is implemented to assess the performance of deep neural networks for segmenting and classifying breast lesions.Data Format and Usage NotesThe BUS dataset is publicly available for academic and research purposes through an open‐access repository under the name BUS‐BRA: A Breast Ultrasound Dataset for Assessing CAD Systems. BUS images and reference segmentations are saved in Portable Network Graphic (PNG) format files, and the dataset information is stored in separate Comma‐Separated Value (CSV) files.Potential ApplicationsThe BUS‐BRA dataset can be used to develop and assess artificial intelligence‐based lesion detection and segmentation methods, and the classification of BUS images into pathological classes and BI‐RADS categories. Other potential applications include developing image processing methods like despeckle filtering and contrast enhancement methods to improve image quality and feature engineering for image description.

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Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network

Shareef,

Xian,

Vakanski

et al. 2023

Lecture Notes in Computer Science

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A Benchmark for Breast Ultrasound Image Classification

Cited by 5 publications

References 0 publications

A Novel Fuzzy Relative-Position-Coding Transformer for Breast Cancer Diagnosis Using Ultrasonography

A Novel Fuzzy Relative-Position-Coding Transformer for Breast Cancer Diagnosis Using Ultrasonography

BUS‐BRA: A breast ultrasound dataset for assessing computer‐aided diagnosis systems

Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network

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